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Handbook of Discourse Processes

Arthur C. Graesser, Morton Ann Gernsbacher, Susan R. Goldman

Text Comprehension

Publication detailshttps://www.routledgehandbooks.com/doi/10.4324/9781410607348.ch3

Rolf A. Zwaan, Murray SingerPublished online on: 01 Feb 2003

How to cite :- Rolf A. Zwaan, Murray Singer. 01 Feb 2003, Text Comprehension from: Handbook ofDiscourse Processes RoutledgeAccessed on: 09 Dec 2021https://www.routledgehandbooks.com/doi/10.4324/9781410607348.ch3

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Comprehending text is part of the daily routine for most individuals 6years of age and older. We read texts for a variety of reasons. We wantto be informed about the state of the world, learn about new domains,escape into fictional worlds, and perform certain actions (e.g., fill outa tax form). The skill to accomplish all these things, based on percep-tual processes by which our eyes fixate black marks on white andthen jump to the next set of marks, is surely one of the most remark-able accomplishments of our species. Text comprehension research-ers are slowly but steadily uncovering the highly complex set of cogni-tive mechanisms that underlie the skill to comprehend text. In doingso, they have developed an impressive range of theories and tools totest them. This chapter provides an overview of this work. The ques-tion of how people convert the proverbial black marks on white to“stories in their heads” is, in and of itself, a fascinating one. In our at-tempts to address this question, we are learning a great deal moreabout what makes us human. After all, the ability to understand lan-guage is shared by no other animal. However, text comprehensionresearch also has a distinct practical relevance. For example, textcomprehension research provides useful information on how docu-ments (e.g., manuals, questionnaires, drug prescriptions, educa-tional texts) can be written so they convey their information with max-imum clarity.

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Text Comprehension

Rolf A. ZwaanFlorida State University

Murray SingerUniversity of Manitoba

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A THEORETICAL FRAMEWORKFOR TEXT COMPREHENSION

A fundamental assumption of this chapter is that text comprehensionis an instance of complex information processing, and that it thereforecomplies with general principles of cognition. In this section, we usethe influential construction-integration theory (Kintsch, 1988, 1998;see also Kintsch, 1974; Kintsch & van Dijk, 1978) as an illustration ofa theoretical framework for text comprehension. Although individualcomponents of construction integration remain controversial, many ofthe principles that it encompasses are generally accepted in the field.

The theory assumes that comprehenders process text one chunk (aclause or sentence) at a time. The processing of each chunk includes aconstruction phase and an integration phase. During construction,lower level processes such as orthographic analysis, word retrieval,and grammatical parsing analyze the current text chunk into ideaunits called propositions (Carroll, 1978; Clark & Clark, 1977;Kintsch, 1974; Townsend & Bever, 1982). For example, the proposi-tions derived from the input sentence Alice unlocked the wooden door

with the key are represented as P1 (UNLOCK, AGENT:ALICE, PA-TIENT:DOOR, INSTRUMENT:KEY), P2 (WOODEN, DOOR). Eachproposition includes one predicate that, using the present notation, iswritten first (e.g., UNLOCK). The proposition also includes one ormore concepts called arguments, each of which fulfills a distinct se-mantic function. The psychological validity of the proposition has beensupported by numerous demonstrations: Text reading time increasessystematically with the number of (a) propositions, holding constantthe number of words (Kintsch & Keenan, 1973); and (b) different argu-ments in the text, holding constant the number of propositions(Kintsch, Kozminsky, Streby, McKoon, & Keenan, 1975). This analy-sis also suggests that arguments appearing in the same propositionare more strongly connected in memory than are concepts from differ-ent propositions (Ratcliff & McKoon, 1978; Weisberg, 1969).

During construction, the propositions that are expressed directlyin the text are organized into a coherence network. This network isproposed to include several other types of ideas. Contained in the net-work are: (a) close associates of text ideas (e.g., both SPY and INSECTare associated with BUG even in the spying context); (b) inferencesthat contribute to the coherence of the text; and (c) text generaliza-tions (e.g., the student sprinkled chalk dust on the chair might begeneralized to PRANK).

During the subsequent integration phase of construction integra-tion, activation accumulates in the network propositions that are most

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highly interconnected with one another. In this process, there is a de-activation of contextually inappropriate concepts, such as the INSECTassociation to BUG in the spying context. This distribution of activa-tion has been analyzed in the form of mathematical algorithms (e.g.,Kintsch, 1988; Rumelhart & McClelland, 1986).

These language and discourse processes continually interact withdifferent memory systems. The processes of the integration phasemodify the original coherence network to produce an enduring long-term memory representation of the text (Kintsch & Welsch, 1991). Atthe end of each construction-integration cycle, a small number ofhighly active elements of the current clause are held over in a limitedcapacity working memory (Baddeley, 1986) for further processing(Fletcher, 1981; Kintsch & van Dijk, 1978). It is by virtue of this carry-over that a coherent text representation may be constructed.

The long-term memory encoding of the text consists of multiple lev-els of representation, the surface structure, a mental representation ofthe actual wording of the text, the textbase, a mental representation ofthe explicitly stated semantic information in the text, and the situation

model, a mental representation of the state of affairs denoted in a text.These levels of representation are inspected later. However, we first ex-amine the methods typically used in text comprehension research.

TEXT COMPREHENSION RESEARCH METHODS

The methods typically used in text comprehension can roughly be di-vided into two categories. The first category taps the comprehensionprocess as it unfolds. These methods are called online methods. Thesecond category of methods, historically older than the first, focuseson the results of the comprehension process, the mental representa-tions stored in the comprehender’s long-term memory. These meth-ods are characterized here as memory methods.

Online Methods

The online methods can be subdivided into four types: processing-

load measures, activation measures, information-content meas-

ures, and brain-activity measures.

Processing-Load Measures

Processing-load measures are used to make inferences about therelative amounts of cognitive resources needed to process linguistic in-formation. This idea relies on the old assumption (e.g., Donders,

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1868/1969) that processes that require more attentional or memoryresources take longer than processes that require fewer resources.There are several ways in which processing times can be measured.Perhaps the most widely used method in text comprehension researchis the self-paced reading task, in which people see segments of a text,typically clauses or sentences, on a computer screen one at a time.They advance through the text by pressing a key on the keyboard or amouse button and the computer measures their reading times. Al-though this method is not as precise as the methods to be discussednext, it is precise enough to tap many of the processes in which dis-course psychologists are interested.

The moving-window task allows more precise measurements. Inthis task, texts are displayed in their entirety (or one page at a time) onthe computer screen, but with all the letters changed into dashes orslashes. By pressing a button or key, the reader makes a word visibleand, by pressing the button again, the word reverts back to dashes andslashes and the next word appears. It is as if the reader is passing asmall window across the masked text. In this fashion, reading timescan be obtained for each word in the text while the reader still has anidea of the overall structure of the text.

The two preceding tasks share the characteristic that they rely onkeypresses for the measurement of reading times. Thus, the resultingreading times reflect not only the time to read the sentence, but also thetime needed to switch attention from comprehension to the key pressand the time needed to execute the motor response. Furthermore,there is the possibility that the response times are not dictated by theamount of cognitive effort involved, but by the tendency to tap the keysin a rhythmic fashion. The eyetracking method does not have thesedisadvantages (see Rayner & Sereno, 1994, for a useful discussion).With this method, texts are presented on a computer screen and a par-ticipant’s eye movements are registered by small cameras as he or sheprocesses the text. Thus, there are no key presses involved. Contraryto most people’s intuitions, reading is not a series of smooth horizontalsweeps of the eyes across a page. Instead, the eye pauses to take in in-formation and then jumps to the next word (some short and highly pre-dictable words such as the are often skipped). The pauses are knownas fixations and the jumps as saccades. A third type of eye movementis known as regressions. Regressions occur when the eye moves backto an earlier word in the sentence. Along with long fixations, regres-sions are viewed as indexes of processing difficulty. Thus, eyetrackingallows more precision than do the other methods. However, often thisprecision is not necessary; given that the method is relatively expensiveand labor intensive (the eyetracker has to be calibrated for each indi-

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vidual participant), many researchers prefer to use the key-pressmethods.

Activation Measures

Various measures are used to measure the availability of informa-tion to readers as they comprehend a text. These measures allow theresearcher an insight into the mental representation as it unfolds inthe comprehender’s mind. The three most commonly used methods toassess activation are: lexical decision, naming, and probe recognition.

Lexical Decisions. In traditional lexical decision experiments (e.g.,Meyer & Schvaneveldt, 1971), people are presented with a string of let-ters and have to determine whether it is a word. A central finding is thatthe lexical decision for a word like doctor is faster when it is immedi-ately preceded by a close associate like nurse than by the unrelatedword bread or by a row of Xs. This implies that nurse activates orprimes its close associate, doctor. In applications of this procedure totext comprehension research, one or more words from the text areused as primes, and lexical-decision latencies are used to make infer-ences about the degree to which concepts are activated. Thus, onecould present the opening sentence of Alice in Wonderland:

1. Alice was beginning to get very tired of sitting by her sister on the bankand having nothing to do.

followed by the lexical decision item bored. The prediction would thenbe that, if people made the inference that Alice was bored, lexical deci-sion times to this word should be shorter than those to a control word.This is because bored was activated during the reading of the sentence,thus facilitating the lexical decision when the actual word appeared onthe screen.

Naming. Similarly, one can assess activation simply by having par-ticipants name words. The assumption here is that priming reducesnaming latencies. It has been argued that naming has the advantageover lexical decision that it does not involve a yes–no decision compo-nent (Potts, Keenan, & Golding, 1988). Another advantage is that theprocedure of the naming test requires fewer items than lexical deci-sion, which requires that about half of the test items be nonwords.

Probe Recognition. A third way to assess activation is by using recog-nition probes. In this case, a word is presented and the participant’stask is to indicate whether it appeared in the text read up to that point.


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Probe-recognition shares with lexical decision the fact that it involves adecision component and thus requires more items than does naming.Furthermore, it could be argued that probe recognition focuses thecomprehender’s attention on the surface structure of the text. None-theless, many experiments have shown that the procedure is sensitiveto situation-model levels of processing. Gordon, Hendrick, and Foster(2000) recently identified a new potential drawback of the probe-recognition task. In some experimental designs, participants seem touse a strategy in which they keep track of the words that might beprobed. However, this problem is likely to be confined to situations inwhich the pool of to-be-recognized words is small.

Information-Content Measures

Activation measures allow the researcher to draw inferences aboutspecific aspects of content and structure of the mental representationthat is being constructed during comprehension. Information-contentmeasures, in contrast, provide much more extensive information.However, the question is to what extent they reflect processes that arereally going on during online comprehension or to what extent they re-flect task demands.

One instance of information content measures is the think-aloudprotocol. Think-aloud protocols have been used extensively in re-search on memory and expertise. In text-comprehension applications,participants are typically presented with one sentence or clause at atime and are instructed to comment on their understanding of the sen-tence or clause in the context of the text they are reading. The think-aloud method has been argued to provide an accurate window on cog-nitive processes provided the task is well defined (Ericsson & Simon,1993). Although this is often the case in problem-solving experiments,it is often not the case in language-comprehension experiments. Forthat reason, think-aloud protocols are most often used as exploratorymethods in conjunction with activation measures, with the latter beingused as hypothesis-testing devices. A good example of this is the studyof Trabasso and Suh (1993). Trabasso and Magliano (1996) developeda detailed procedure for analyzing think-aloud protocols (see alsoMagliano & Graesser, 1991).

Another way to elicit information is by using a question-answeringprocedure in which participants are asked specific questions about as-pects of a text (e.g., why did X happen?). Graesser and Clark (1985)used this procedure to uncover the range of information that might beactivated during online comprehension of a particular text segment. Itis also possible to elicit information about the contents of compre-

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henders’ mental representations by asking them to complete a textfragment. Presumably, some linguistic devices invite different continu-ations than others. For example, Gernsbacher and Schroyer (1989)showed how a cataphoric pronoun such as this (e.g., the egg in On the

beach she found this egg) prompted participants to maintain its refer-ent in their continuations to a greater extent than the indefinite pro-noun an (as in On the beach she found an egg).

Content measures may provide useful insights into text comprehen-sion. However, they are most profitably used as exploratory devices inconjunction with hypothesis-testing tools for tapping online proc-esses, such as processing-load and activation measures. This is be-cause of the potential vulnerability of content measures to strategicprocesses on the part of the participants.

Brain Activity Measures

A recent development in the study of discourse comprehension isthe use of brain-activation measures. The method of measuring event-related potentials (ERP), by attaching electrodes to a participant’sscalp, has been used for quite some time in the study of language proc-esses. However, until recently, most of its applications were at the lev-els of the word or sentence. A pattern of electrical activity of particularrelevance to language comprehension is known as the N400 (e.g.,Kutas, Federmeier, & Sereno, 1999; Kutas & van Petten, 1994), whichis a shift in negative activity occurring about 400 ms after the presenta-tion of a stimulus. The N400 is thought to reflect difficulty in semanticintegration. For example, in the sentence He dug the hole with a

pizza, pizza will yield an N400 with a larger amplitude than if the lastword were shovel. Recently, van Berkum, Hagoort, and Brown (1999)demonstrated that contradictions in discourse yield robust N400 ef-fects, which suggests that ERPs might be a useful tool in the study ofdiscourse comprehension. ERPs have several advantages over reac-tion-time methods, the main one being their temporal resolution.Whereas the shortest response latencies in reaction time experimentsare between 350 and 400 ms (in naming tasks), ERPs show effects atshorter latencies (e.g., although N400 reaches its peak at about 400ms, the onset is much earlier). ERPs provide some information aboutwhere in the brain processes are localized, although the spatial resolu-tion of ERPs is rather low compared with brain-imaging methods. Adrawback of ERPs is that, to obtain stable patterns within partici-pants, large numbers of items (at least 30) are needed per condition.

Brain imaging methods such as positron-emission tomography(PET) and functional magnetic resonance imaging (fMRI) allow the re-


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searcher to localize cognitive processes in the brain. As such thesemethods hold great promise with respect to uncovering the neural sub-strates of discourse comprehension. Neuroimaging methods havebeen used in several recent studies of text comprehension (Carpenter,Just, Keller, Eddy, & Thulborn, 1999; Fletcher et al., 1995; Maguire,Frith, & Morris, 1999; Robertson et al., 2000). Among other things,these studies converge on the rather surprising finding that areas inthe right hemisphere (and not the traditional language areas in the lefthemisphere) are involved in the integration of information across sen-tences and in the construction of spatial representations from text. Al-though neuroimaging research is clearly still in its infancy and variousmethodological hurdles have to be overcome, it is likely that neuro-imaging studies of text comprehension will soon begin to constraintheorizing about language comprehension.

Memory Measures

Memory measures provide information on how the mental representa-tion constructed during comprehension is stored in and retrievedfrom long-term memory. The method of free recall is one of the firstmethods used to study discourse comprehension (Bartlett, 1932).However, questions have arisen regarding the extent to which recallprovides a window on the long-term memory representation laid downas a result of the comprehension process (e.g., Corbett & Dosher,1978). As Bartlett noted, recall is a constructive process. Thus, what isor is not in the recall protocol does not necessarily reflect what wasconstructed during comprehension. For example, people may remem-ber information and then decide to edit it out of their recall protocolbecause they think it does not fit very well. However, having to recall in-formation from texts (oral or print) is a daily activity for most people.Therefore, studying recall from text is an important topic in and of it-self. Memory for text is discussed in a later section.

In cued recall, the participant is presented with part of a discourseand is asked to provide a missing part, such as the second sentence ofa pair. Such tasks have yielded important insights into how informa-tion is integrated in long-term memory (e.g., Myers, Shinjo, & Duffy,1987). As discussed later, recognition (“Did you see this sentence be-fore?”) and verification (“Is this statement true with respect to whatyou just read?”) are used to assess the relative strengths of differentlevels of representation.

When measures are not timed, it is always possible that they arecontaminated by strategic processes. Ratcliff and McKoon (1978) de-veloped the method of primed recognition, which is not sensitive or is

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much less sensitive to strategic processes. In this procedure, sets ofstatements pertaining to texts are shown, one at a time, to participants.Their job is to indicate as quickly and accurately as possible whethereach sentence occurred in the text. Each set of items contains a prime-target pair, sentences from the text that the researchers hypothesizeare more related in one condition than in one or more others. The rec-ognition latencies are thought to reflect the strength of the long-termmemory link between the nodes coding for the statements or events de-noted by the prime-target pair (e.g., Zwaan, 1996).

MULTILEVEL REPRESENTATIONS

Various theorists have argued that, during the comprehension of texts,readers construct a mental representation of the text as well as the sit-uations described in the text. For example, van Dijk and Kintsch(1983) proposed that readers construct mental representations of (a)the text’s surface structure, (b) the semantic meaning explicitly con-veyed by the text or textbase, and (c) the situation described in the text,the situation model. The first two levels—surface structure andtextbase—are sometimes collapsed. For example, Johnson-Laird’s(1983, 1996) propositional representation appears to be an amalgamof the surface structure and textbase. Researchers have not agreed ona single representational format for situation models. For example,Kintsch (1998) viewed situation models largely as propositional repre-sentations, but allowed mental images as well. Johnson-Laird (1996)viewed mental models as nonpropositional, but also as different frommental images. Barsalou (1999) proposed an entirely different view, inwhich the building blocks of mental representation are not abstractand amodal, but analog representations called perceptual symbols

that are the result of perceptual activity in the brain. Thus, at the mo-ment, situation models are less characterized by their structure thanby their content. Much of the effort in research on situation models hasgone into delineating which aspects of described situations are en-coded in situation models and which are not.

SURFACE AND TEXTBASE REPRESENTATIONS

Empirical evidence suggests that readers typically maintain a briefmemory record of the wording of a text (J. Anderson, 1974; Bransford,Barclay, & Franks, 1972; Clark & Sengul, 1979; Gernsbacher, 1985;Graesser & Mandler, 1975; Jarvella, 1971a, 1971b; Kintsch & Bates,1977; Sachs, 1967). Estimates are that, under most conditions, decay


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of the surface representation can be measured in seconds. However, insome cases, surface representations are maintained over long periodsof time. In a series of experiments that ruled out various alternative ex-planations, Murphy and Shapiro (1994) showed that a critical factor inverbatim memory is the pragmatic context of a sentence. If a sentencehas high interactive value (e.g., if it is an insult or a joke), thecomprehender’s attention is focused on its wording, leading to betterencoding and thus to a better surface memory (see also Kintsch &Bates, 1977). Similarly, Zwaan (1993, 1994) showed that surfacememory can be affected by comprehenders’ expectations about thegenre of a text they are reading. When participants thought they werereading excerpts from novels, they exhibited better surface memorythan when they thought they were reading newspaper articles. In bothcases, the same texts were used, ruling out wording as a factor. How-ever, this does not mean that wording plays no role. For example, po-etry and songs provide constraints on fit, other than semantics—namely, meter and rhyme. Consequently, people exhibit excellent sur-face memory for songs and poems (Rubin, 1995).

The textbase is thought of as the explicitly stated meaning of the text.Kintsch and van Dijk (1978) developed an influential model of textbaseconstruction, whose function it was to predict recall from text. In thismodel, texts were hand parsed into propositions, and the propositionswere arranged into a network according to several principles. For ex-ample, a connection between two propositions was made only if theyco-occurred in a working memory buffer. Mechanisms such as theleading-edge rule specified that it is predominantly the most recentand important propositions that are held over in the working memory.With appropriate assumptions about the size of the buffer, the Kintschand van Dijk model proved accurate at predicting recall from text.However, van Dijk and Kintsch (1983) noted that their earlier modelfailed to capture the most important aspect of comprehension: theconstruction of a situation model.

SITUATION MODEL REPRESENTATIONS

Why is it necessary to posit situation models? Why is it not sufficient toassume that comprehenders construct a textbase? The following ex-ample from Sanford and Garrod (1998) suggests an answer.

2. Harry put the wallpaper on the table. Then he put his mug of coffee onthe paper.

It is rather straightforward to integrate these sentences. They call for aspatial arrangement in which the paper is on top of the table, the mug

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is on top of the paper, and the coffee is inside the mug. However, con-sider the following sentence pair, which differs from (2a) by only oneword:

3. Harry put the wallpaper on the wall. Then he put his mug of coffee onthe paper.

Most readers balk at the second sentence. Surely it is impossible toput a mug of coffee on a vertical surface. If one simply assumes thatcomprehenders construct propositional textbases, then no such effectwould be expected. The sentence pairs are equivalent in their proposi-tional structure and connections.

Various experiments have shown that equivalent propositionalstructures lead to different behavioral responses. Many of these stud-ies were reviewed by Zwaan and Radvansky (1998). There is evidencethat comprehenders keep track of at least five situational dimensionsduring comprehension: time, space, characters, causation, and moti-vation. Zwaan and Radvansky (see also Zwaan, Magliano, & Graesser,1995) proposed that the building blocks of situation models are men-tal representations of single events. These event representations areintegrated during comprehension based on their overlap on each of thesituational dimensions. Thus, an event that occurred at the same timeand place as the previous event will, all other things being equal, bemore easily integratable into the evolving situation model than an eventthat takes place at a different time or place. By the same token, twoevents that overlap on multiple situational dimensions are morestrongly connected in the comprehender’s long-term memory repre-sentation than two events connected on only a single dimension. It hasbeen shown that readers simultaneously monitor multiple situationaldimensions during comprehension and that this is reflected in theirlong-term memory representations (Zwaan, Langston, & Graesser,1995; Zwaan, Magliano, & Graesser, 1995; Zwaan, Radvansky,Hilliard, & Curiel, 1998). However, in most research on situation mod-els, the focus is on single dimensions.

Spatial Situations

The dimension that received the most attention in early research onsituation models was space. A decade prior to the coining of the termsmental model and situation model, Bransford, Barclay, and Franks(1972) demonstrated empirically that the spatial structure of the de-scribed situation can have a powerful effect on the comprehender’smemory. In this study, the participants listened to sentences such as 4


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and 6. Afterward they were presented with sentences such as 5 and 7 ina surprise recognition test.

4. Three turtles rested on a floating log, and a fish swam beneath them.5. Three turtles rested on a floating log, and a fish swam beneath it.6. Three turtles rested beside a floating log, and a fish swam beneath

them.7. Three turtles rested beside a floating log, and a fish swam beneath it.

People who had heard 4 frequently incorrectly recognized 5,whereas people who had heard 6 rarely incorrectly recognized 7. Thisdiscrepancy cannot be explained by differential changes at the surfacestructure level of the test items. The only surface structure differencebetween the members of the pairs 4–5 and 6–7 is that the pronounthem has been replaced with it. However, the pairs differ with respectto the spatial layout they describe. Sentences 4 and 5 describe essen-tially the same situation: The turtles are on top of the log and the log isabove the fish. Sentences 6 and 7, in contrast, describe different spa-tial situations. Sentence 6 has the fish beneath the turtles but not thelog, whereas 7 has the fish beneath the log but not beneath the turtles.Thus, 4 and 5 are being confused because they describe the same situ-ation. In contrast, 6 and 7 are less likely to be confused because theydescribe different situations.

Many more recent experiments have examined the role of spatialrepresentations in text comprehension (see Zwaan & Radvansky,1998, for a review). The most accurate characterization at this mo-ment is that readers spontaneously construct spatial representationsof some sort, but these spatial representations are often not detailedunless at least one of the following holds: (a) The reader has detailedprior knowledge about the spatial layout of the environment in which astory takes place, or (b) the reader is instructed, constrained, or intrin-sically motivated to construct a detailed spatial representation.

Temporal Situations

More recently, researchers have started investigating the time dimen-sion (A. Anderson, Garrod, & Sanford, 1983; Bestgen & Vonk, 2000;Carreiras, Carriedo, Alonso, & Fernandez, 1997; Mandler, 1986;Münte, Schiltz, & Kutas, 1998; Zwaan, 1996; Zwaan, Madden, &Whitten, 2000). Time is the situational dimension that is the most ex-plicitly coded in language. For example, in languages such as Englishand French, a tense morpheme is attached to the main verb of eachsentence, specifying the temporal position of the described event rela-

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tive to the moment of utterance. Temporal markers such as timeadverbials (e.g., “a few minutes later”) locate events even more pre-cisely in time. The cited studies show that temporal markers haverather subtle effects on comprehension. Together the results of thesestudies suggest that comprehenders use temporal markers to organizenarrated events into meaningful structures and that these markershave rather immediate effects on processing (Münte et al., 1998).

COHERENCE AND INFERENCE

To be comprehensible, texts must be coherent: Readers must be ableto identify relations among the text ideas. The issue of coherence per-vades the study of many central phenomena of text comprehension asbecomes clear in this section.

The given-new analysis is a central principle of text coherence(Clark & Haviland, 1977; Haviland & Clark, 1974). Most sentencesconvey both given and new information. For example, the grammaticalconstruction of What Alice painted were the roses conveys that thegiven information is that Alice painted something. The new informa-tion is that roses were painted. To fully grasp a sentence, a given-newstrategy must be executed: The reader must distinguish the given andnew information underlying a sentence, identify—in memory—a refer-ent for the given idea, and link the new information to that referent.

The given and new ideas that underlie sentences are differentiatedby many text characteristics (Clark & Clark, 1977). The grammaticalstructure of What Alice painted were the roses performs this func-tion. Likewise, in A dormouse drank the tea, the definite article the

designates tea as given information, whereas the indefinite article a

signifies dormouse as new information. To be pragmatically coopera-tive, the writer must, by means of various devices, distinguish givenand new sentence information in a manner that best coincides with hisor her beliefs about the reader’s knowledge and the previous discoursehistory.

Coherence and Coreference

In identifying the referent of the given information, the reader estab-lishes that the given idea and its referent corefer to the same entity inthe world. Coreference can be signaled by many linguistic devices andsemantic relations. Consider the sentence, The dormouse drank the

coffee. This sentence has many possible continuations, such as The

coffee . . ., The java . . ., and The beverage. . . . These continuationsare, respectively, recognized as coreferential with the coffee by virtue


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of lexical identity (coffee-coffee), synonymy, and category relations.Each continuation (e.g., The beverage . . .) is a definite noun phrase—that is, the nouns are modified by the definite article the. These nounphrases function as anaphors—expressions that refer back to an en-tity or constituent previously denoted in the text.

Anaphoric Resolution

The prototypical anaphor is the pronoun. In The dormouse drank the

tea. It was tasty, it constitutes a definite pronoun that is coreferentialwith tea. Extensive research concerning the role of definite pronounshas imparted many general principles of anaphoric resolution. A cen-tral finding is that the surface, textbase, and situational representa-tions of texts individually and interactively influence pronoun resolu-tion.

In their surface expression, pronouns vary in gender (she–he),number (she–they), and person (she–I). As a result, pronouns may(sentence 8) or may not (sentence 9) unambiguously signify their refer-ents.

8. Sally rewarded Ron because he was on time.9. Tom rewarded Ron because he was on time.

Sentences like 8 with pronouns that, by virtue of their syntacticcharacteristics, have a single referent and take less time to read thanthose with multiple candidate referents (Ehrlich, 1980; Frederiksen,1981; Springston, 1975). Likewise, the pronoun is understood morequickly when the text provides only one referent (Caramazza, Grober,Garvey, & Yates, 1977; Vonk, 1985).

If anaphoric resolution were a strictly accurate process, then theanaphor would access only its correct referent. In that event, the pro-noun he in 10 would access only KEVIN:

10. Gary gave Kevin a lot of money and he spent it foolishly.

In a test of this hypothesis (Corbett, 1984), people read sentences suchas 10 and 11, and then had to indicate whether a test name, such asGary, appeared in the sentence.

11. Gary gave Kevin a lot of money and Kevin spent it foolishly.

People needed less time to recognize Gary after sentence 10 than 11,which suggested that the pronoun he accessed GARY as well as KEVIN.

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A general finding in this realm is that if a text provides several candi-date referents, the pronouns temporarily access all of them (Corbett &Chang, 1983; McKoon & Ratcliff, 1980).

Another surface characteristic that influences pronoun resolutionis the physical distance in the text between the pronoun and its refer-ent. In sentence 12c, the only possible referent for the pronoun it isBOOK, but the pronoun and its antecedent are separated by interven-ing sentence b.

12. a. Yesterday I met a woman who had written a book on viruses.b. She had studied them for years and years.c. It was selling very well.

People take longer to read sentence 12c in a sequence such as 12a–cthan when book appears in the sentence immediately preceding 12c(Lesgold, Roth, & Curtis, 1979; see also McKoon & Ratcliff, 1980;O’Brien, Duffy, & Myers, 1986). This outcome is likely a joint functionof (a) the deletion of BOOK from working memory on reading 12b, and(b) the change of focus away from BOOK in sentence b.

Semantic factors interact with surface variables in pronoun resolu-tion as suggested by 13.

13. Clinton confessed to Archie because he wanted forgiveness.

Although Archie is the most recent noun that agrees syntactically withhe, the implicit causality of the verb confessed guides the resolution ofhe to Clinton—that is, it is usually the characteristics of the confessorthat prompt confessing. In a study designed to probe these issues, par-ticipants read sentences with because clauses that were consistent(sentence 13) or inconsistent (sentence 14) with the implicit causalityof a preceding verb (Caramazza et al., 1977).

14. Clinton confessed to Archie because he offered forgiveness.

The participant had to judge whether a pronoun in the because clausereferred to the first or second noun in the preceding clause. Consistentwith their analysis, Caramazza et al. found that pronoun resolutiontook longer in the inconsistent sentences than consistent sentences(see also Au, 1986; Ehrlich, 1980; Matthews & Chodorow, 1988;Springston, 1975). Another semantic relation that influences ana-phoric resolution is the category link between text elements (Corbett,1984; Garrod & Sanford, 1977). For example, reading times for sen-tences containing a definite anaphor denoting a category (e.g., the vehi-


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cle) are longer when the antecedent is an atypical member of the cate-gory (e.g., a tank) than when it is a typical member (e.g., a bus).

Finally, pronoun resolution is influenced by the situation model of atext. As discussed earlier, the different dimensions of the situationmodel, such as time, spatial location, and character, may interact incomplex ways. For example, after the shift from one situational epi-sode to another, it is difficult to resolve a pronoun that refers to a mi-nor narrative character mentioned in the former episode. However, re-solving a pronoun to the main character who was last mentioned in theprevious episode does not pose difficulty (A. Anderson et al., 1983). Inanother study that addresses narrative situation models, people readpassages in alternate versions, such as ones that included either 15b

or 15b (Morrow, 1985).

15. a. Paul caught the flu and was feeling pretty awful. He told his eldestson Ben to keep the house quiet. He got up from bed to go to thebathroom, irritated by the noise. (. . .)

b. That noisy Ben was messing up the kitchen.

b . Ben was wondering when his father would feel better as he ate inthe kitchen.

c. The floor felt cold on his feet.

The readers generally interpreted the pronoun his in c to refer to themain character (Paul) even in a version of the passage that mentionedthe son (Ben) more recently (e.g., sentence 15b). Only when the per-

spective was changed to that of Ben (15b ) was Ben preferred as the ref-erent of his (see also Malt, 1985).

The relative contributions of the surface, textbase, and situa-tional representations to anaphoric resolution have been subjected toempirical scrutiny. Garnham, Oakhill, Ehrlich, and Carreiras (1995)showed that when definite anaphors appear in text shortly after theirantecedent, surface (e.g., gender agreement) and situational represen-tations make independent contributions to resolution. This contra-dicted a linguistic hypothesis that stated that pronoun anaphors areresolved exclusively on the basis of a referential situation model (Sag &Hankamer, 1984). Garnham et al. also reported that, with more inter-vening text, the contribution of the surface level quickly diminished.

In other instances, the situation model appears to predominate.Consider sequences 16 and 17:

16. I was really frightened by a Doberman. (They are dangerous beasts./It is a dangerous beast.)

17. Last night we went to hear a new jazz band. (They/it) played fornearly five hours.

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The they versions of 16 and 17 are ostensibly ungrammatical be-cause the pronouns disagree in number with their antecedents. How-ever, Gernsbacher (1991) proposed that the first sentence of each se-quence supports a situation model that can function as a referent forthe plural they. In 16, a Doberman signifies the category of all Dober-mans, and in 17, band consists of a number of musicians. In fact, peo-ple needed less time to read and assigned higher naturalness ratings tothe they versions of 16 and 17 than the it versions (Oakhill, Garnham,Gernsbacher, & Cain, 1992).

Toward a Model of Anaphoric Resolution. The present treatment mightsuggest the following simple analysis of anaphoric resolution: “Ana-phors constitute retrieval cues for their referents. Anaphoric resolu-tion is guided by a variety of syntactic, semantic, and discourserelations between anaphor and antecedent.” However, even aside fromthe aforementioned contributions of situation models to anaphoricresolution, other anaphoric phenomena reveal this analysis to be aconsiderable oversimplification. First, if anaphors function mainly asretrieval cues for their antecedents, then pronouns should be readilyreplaceable with corresponding noun phrases. As a result, sentences18 and 19 should be equally felicitous.

18. Does Bob think that his performance will go well?19. Does Bob think that Bob’s performance will go well?

It is apparent, however, that sentence 19 is either ungrammatical or re-fers to two different Bobs (Halliday & Hasan, 1976).

Second, the use of definite noun phrase and pronoun anaphors isinterwoven with issues of discourse focus. In this regard, Vonk,Hustinx, and Simons (1992) showed that when people are asked tocontinue a story with a given word, pronouns prompt people to main-tain the current text focus, whereas noun phrases (character names inthe Vonk et al. study) initiate a topic shift. Conversely, comic stripsthat depicted topic continuity favored participants’ descriptions begin-ning with a pronoun, whereas those depicting topic shifts were de-scribed with names (Vonk et al., 1992, Experiment 2).

Furthermore, pronouns are preferred to definite descriptions forreferents that form the current text focus (Gordon & Scearce, 1995). Ina revealing study of this phenomenon, Almor (1999, Experiment 1) ex-amined sequences like 20 and 21:

20. It was the robin that ate the apple. The bird seemed very satisfied.21. What the robin ate was the apple. The bird seemed very satisfied.


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The grammatical constructions (called clefts) of 20 and 21, respec-tively, designate robin and apple as the new (or focused) elements.People needed less time to read the anaphoric noun phrase the bird in20 than 21. More important, Almor (1999, Experiment 3) observed theopposite pattern when the anaphor repeated the expression of its ante-cedent:

22. It was the bird that ate the apple. The bird seemed very satisfied.

Earlier, sequence 19 illustrated that the repetition of noun phrasescan be grammatically unsuitable. Together these results show that thesuitability of noun phrase repetition depends on the current conditionof focus. Almor presented these observations in the framework of theproposal that anaphoric phenomena are regulated by the processingcost of identifying the referent and computing the new information sig-naled by the anaphor. This offers a useful step toward a unified modelof anaphoric reference, and hence a deeper understanding of text co-herence.

Inference Processes

Almost every facet of comprehension is at least partly inferential: Com-plex inferences are needed to identify the intended meaning of ambigu-ous words (The farmer filled the PEN), the grammatical analysis ofsentences (The old man the boats), and the thrust of ordinary com-ments (Do you have a watch?). As a result, inference processing hasbeen a major focus of investigation for the past 30 years.

In this section, inferences in the textbase and situation model repre-sentations are scrutinized. If from the sentence Alice unlocked the

door the reader inferred that Alice used a key, the resulting textbaserepresentation might be (UNLOCK, AGENT:ALICE, PATIENT:DOOR,INSTRUMENT:KEY). In contrast, understanding The lightning struck,

The hut collapsed might require an inference in the causal situation

model about the relationship between the two events.

Bridging Inferences

Of central importance are bridging inferences, which link the currentclause to the preceding text. Consider sequence 23:

23. a. The pitcher threw to first base.b. The ball sailed into the field.

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In keeping with the given-new analysis, understanding 23b requiresidentifying the referent of the given information, the ball. Sentence 23adoes not mention a ball, but the reader can draw on world knowledgeto determine that the ball was the object that the pitcher threw. Meas-ures of reading time indicate that the processes of bridging a definitenoun phrase (the ball) to the prior text are more consuming of cogni-tive resources than resolving anaphors based on identical phrasing,synonyms, and pronouns (Haviland & Clark, 1974; Lesgold et al.,1979).

Similarly, understanding sequence 24 depends on detecting acausal relation between its two sentences (Black & Bern, 1981).

24. The boy walked over to the refrigerator, bumping a bowl he had lefton the table. Suddenly, it fell off the edge and broke.

By identifying the links between the current and prior text, bridginginferences preserve text coherence. In their absence, ordinary textwould appear as disjointed as Alice unlocked the door. The ball sailed

into the field. Evidence derived from numerous measures indicatesthat bridging inferences routinely accompany comprehension. First,Black and Bern (1981) reported that causally related sentences aremore effective recall cues for one another than are similar temporallyrelated sentences (e.g., obtained by replacing bumping with seeing insequence 24). They concluded that readers inferentially integrate thesentences of 24.

Second, reading time has been used to probe the bridging of the fol-lowing sequences:

25. Tony’s friend suddenly pushed him into a pond. He walked home,soaking wet, to change his clothes.

26. Tony met his friend near a pond in the park. He walked home, soak-ing wet, to change his clothes.

According to rating data, the sentences of 25 are causally closer thanthose of 26. Reading time for the second sentence of such sequencessystematically increased with causal distance (Keenan, Baillet, &Brown, 1984; Myers et al., 1987). This outcome was interpreted to re-flect the readers’ execution of bridging inference processes.

Other investigations of bridging inference have used stimulus mate-rials such as the following:

27. The spy threw the report in the fire. The ashes floated up the chimney.28. The spy threw the report in the fire. Then he called the airline.


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Both 27 and 28 permit the inference that the spy burned the report,but only in 27 is this inference needed to bridge the ashes to the firstsentence. People need more time to answer Did the spy burn a report?

after sequence 28 than 27. Furthermore, answer time for 27 is indis-tinguishable from that observed when the first sentence explicitlystates that the spy burned the report (Singer & Ferreira, 1983). Like-wise, naming time for a word representing the central inference(BURN) is similar in an explicit condition and after the bridging se-quence 27, but longer after sequence 28 (Potts et al., 1988). These re-sults favor the conclusion that bridging inferences reliably accompanycomprehension. The status of inferences such as those underlying se-quence 28 are examined later in the section on Elaborative Inferences.

Theoretical Interpretations. The fundamental conclusion that bridginginferences routinely accompany comprehension is relatively uncon-troversial. However, the development and comparison of two contem-porary theories of comprehension and inference—memory-based textprocessing and constructionism—have focused on more subtle as-pects of the computation of bridging inferences. The central feature ofthe memory-based analysis is that the current clause, plus additionalideas carried over in working memory, function implicitly as memorycues (e.g., McKoon, Gerrig, & Greene, 1996; O’Brien, Lorch, & Myers,1998). In this capacity, the current clause induces the activation ofprior text and relevant world knowledge by means of passive reso-nance processes (Ratcliff, 1978) on the basis of surface form or seman-tic similarities. Consistent with this analysis, the current text canrestore, to working memory, matching concepts from earlier in the textand ideas associated with those concepts. A strong version of the mem-ory-based processing analysis is that comprehension computationsare restricted to (a) ideas carried over in working memory, (b) currenttext, and (c) ideas that have resonated to the current text (Albrecht &Myers, 1995). The memory-based analysis receives support from thefindings that people’s access to a text idea is regulated by its recencyand degree of elaboration in the text and its similarity to the contents ofworking memory (Albrecht & Myers, 1995; O’Brien & Albrecht, 1991;O’Brien, Albrecht, Hakala, & Rizzella, 1995).

The competing constructionist theory has as its central principlethat readers engage in a search after meaning (Bartlett, 1932; Brans-ford et al., 1972; Graesser, Singer, & Trabasso, 1994). Two assump-tions of constructionism are that readers (a) monitor coherence at alllevels of text representation, and (b) routinely seek explanations fortext outcomes such as physical effects and intentional actions. Thethrust of these assumptions is that the computations of comprehen-


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sion do not strictly proceed from lower representations, such as sur-face form, to higher ones, such as the situation model. In fact, situa-tional inferences may sometimes take precedence (Barton & Sanford,1993; Sanford & Garrod, 1998).

Constructionist theorists have proposed that causal situation mod-els take the form of networks of text events interconnected by links ofphysical, motivational, and psychological causation. Consistent withthis analysis, memory for text and importance ratings of text ideas areinfluenced by the degree of connectedness of these ideas and by theirappearance on the main causal chain underlying the text (Trabasso,Secco, & van den Broek, 1984; Trabasso & Sperry, 1985). The need toinferentially link a text statement to other, causally related ideas, is as-sociated with higher reading times for those statements (Bloom,Fletcher, van den Broek, Reitz, & Shapiro, 1990). That result con-verges with the finding that achieving a complete understanding of asentence entails the validation, with reference to world knowledge, ofcandidate causes of a text outcome (Singer, Halldorson, Lear, &Andrusiak, 1992).

The representation of the situation models of goal and cause thatunderlie text has provided a field for comparing memory-based textprocessing and constructionism. Several findings have been invokedas support for memory-based processing. In one study, people readpassages such as one describing Mary as having to make an air reser-vation but getting sidetracked (Albrecht & Myers, 1995). Later she isdescribed as going to bed—an action that is inconsistent with the goalof booking a flight. However, the detection of this inconsistency, as in-dexed by the reading time for the second event, depended on surfaceoverlap between the two critical clauses. In a similar vein, readersoverlooked a nearby cause for a father becoming angry (“broken win-dow”) when the text provided a different, highly elaborated cause (“lostkeys”) earlier (Rizzella & O’Brien, 1996, Experiment 2a). In both stud-ies, the authors concluded that the results were consistent with thememory-based explanation and inconsistent with the constructionistexplanation because the absence of surface overlap (Albrecht & Myers,1995) and presence of a competing, elaborated cause (Rizzella &O’Brien, 1996) prohibited the detection of text causes.

In another study, the description of a character eating a cheese-burger provided access to the concept VEGETARIAN even when thecharacter had been described as no longer being a vegetarian or evennever having been one (O’Brien, Rizzella, Albrecht, & Halleran, 1998;see also Gerrig & McKoon, 1998). Consistent with memory-basedprocessing, this result indicates that the semantic relation betweenCHEESEBURGER and VEGETARIAN is sufficient to reinstate VEGE-


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TARIAN to working memory even when it has little bearing on the pres-ent context. Gerrig and McKoon (1998) claimed that the retrieval oftext ideas not germane to the current causal structure of the text (e.g.,VEGETARIAN in this example) contradicted constructionism.

However, consistent with constructionism, there have been numer-ous demonstrations that readers detect cause and goal relations thatspan moderate text distances. In these studies, either the causal condi-tions have been compared with control conditions that are matchedfor the degree of surface overlap (Long, Golding, & Graesser, 1992;Singer & Halldorson, 1996; Suh & Trabasso, 1993; van den Broek &Lorch, 1993) or surface overlap between the current outcome and itsdistant cause was absent (Richards & Singer, 2001). In another study,the amount of intervening text did not influence the detection of the re-lation between a text outcome and its antecedent cause (Lutz &Radvansky, 1997). This outcome was argued to challenge a tenet ofmemory-based text processing.

The distinction among these and other theoretical analyses (seeGraesser et al., 1994, for a review) remains controversial. However,consideration should be given to the possibility that memory-basedand constructionist processing constitute complementary rather thancompeting analyses. The impact on comprehension of passive reso-nance processes has been effectively scrutinized in the framework ofthe memory-based analysis, but those processes are not inherently in-consistent with the constructionist theory (Graesser et al., 1994). Con-versely, the detection of complex, situational relations need not be pro-hibited in the memory-based framework (Rizzella & O’Brien, 1996).

Elaborative Inferences

Earlier it was recounted that people correctly answer Did the spy burn

the report? faster after sequence 29 than sequence 30 repeated here.This indicates that readers inferentially bridge the sentences of 29.

29. The spy threw the report in the fire. The ashes floated up the chim-ney.

30. The spy threw the report in the fire. Then he called the airline.

The implication that the spy burned the report is also carried by se-quence 30, but text coherence does not depend on that inference. Ideasthat are strongly implied by a discourse context but do not bear on co-herence are called elaborative inferences.

Considerable evidence based on measures of cued recall (Corbett &Dosher, 1978), answer times (Singer, 1980), and speeded judgments


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of single words (McKoon & Ratcliff, 1986; Potts et al., 1988) has indi-cated that elaborative inferences, perhaps counter to intuition, do notreliably accompany comprehension. These studies scrutinized infer-ences ranging from implied roles, such as the participation of the po-lice when a burglar was arrested, to causal predictions, such as thebreaking of a delicate vase when it is described as being dropped.These results have been understood in terms of the observation thatevery message suggests so many plausible inferences that to compute,during comprehension, all of them would overwhelm the available cog-nition resources: In other words, a computational explosion would re-sult (Charniak, 1975; Rieger, 1975).

There is, however, accumulating evidence that certain elaborativeinferences accompany comprehension. It is instructive to considersome prominent examples. First, people appear to encode implied se-mantic features that are especially relevant to text. For example, peo-ple verify Tomatoes are red faster than Tomatoes are round afterreading a text that emphasizes the color of tomatoes and vice versa forone that emphasizes the shape of tomatoes (McKoon & Ratcliff, 1988).

Second, people elaborate category terms to their specific members.Consider sentence 31:

31. Julie was convinced that spring was near when she saw a cute red-breasted bird/robin in her yard.

When sentence 31 uses the category term bird, people generate theelaborative inference ROBIN as is evidenced by reading times for a con-tinuation sentence (Garrod, O’Brien, Morris, & Rayner, 1990; McKoon& Ratcliff, 1989, Experiment 2; O’Brien, Shank, Myers, & Rayner,1988), speeded recognition (McKoon & Ratcliff, 1989), and cued recall(R. Anderson, Pichert, Goetz, Schallert, Stevens, & Trollip, 1976; seealso Dubois & Denis, 1988; Whitney, 1986).

Third, it was mentioned earlier that people do not reliably drawelaborative inferences about implied roles. In one study (Lucas,Tanenhaus, & Carlson, 1990), however, people heard sequences suchas 32 over headphones.

32. a. There was a broom in the closet next to the kitchen.b. Bill swept the floor every week on Saturday.

The participants were required to make lexical decisions about im-plied instrument words such as broom, which for this sequence ap-peared on a screen coincident with the end of the spoken word week.Lexical decision time was lower for implied instruments than for con-


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trol words. Thus, although an inference about a broom does not rou-tinely accompany the comprehension of 32b, it does when the relevantconcept has been made available by the text (see also McKoon &Ratcliff, 1992).

Consistent with the latter observations, readers may also encodeimplied semantic roles into text representations. In this regard,Mauner, Tanenhaus, and Carlson (1995) proposed that sentence 33ais grammatically more suitable than sentence 33b.

33. a. The game show wheel was spun to win a prize.b. The game show wheel spun to win a prize.

Rationale clauses such as to win a prize require the participation of anagent. The short passive construction The game show wheel was

spun implies the involvement of an agent, but the intransitive The

game show wheel spun does not. Consistent with this analysis, in peo-ple’s examination of to win a prize, reading time was longer and thenumber of “does not make sense” responses greater in sentence 33bthan 33a.

Fourth, there is evidence that people draw elaborative inferencesabout text themes. For example, 1 second after encountering the lastword of The townspeople were amazed to find that all the buildings

had collapsed except the mint, people made faster lexical decisionsabout earthquake than when the same word followed a control sen-tence (Till, Mross, & Kintsch, 1988).

Elaborative Inferences and the Construction-Integration Analysis. The con-struction-integration model (Kintsch, 1988) provides a framework forunderstanding the circumstances in which elaborative inferences reli-ably accompany comprehension. Recall that the reader initially con-structs a coherence network consisting of explicit text propositionsand their close associates, coherence preserving inferences, and the-matic generalizations. During integration, activation accumulates inthose elements of the coherence network that are highly intercon-nected. Consider, in this context, some of the classes of elaborative in-ferences just examined. On reading sequence 34, the concept REDmight be constructed as an associate of TOMATO.

34. The still life would require great accuracy. The painter searchedmany days to find the color most suited to use in the painting of theripe tomato.


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However, RED would also bear links to COLOR, the compound con-cept RIPE TOMATO, and other text ideas. As a result, it would remainhighly activated after the integration process. ROUND might be simi-larly constructed after reading 34, but, in the absence of connectionwith many text ideas, would likely not survive integration.

Likewise, the sequence There was a broom in the closet next to the

kitchen. Bill swept the floor every week on Saturday explicitly de-notes BROOM. The joint presence in working memory of BROOM,SWEEP, and FLOOR might be sufficient to result in the encoding of in-ferential links among them.

There is experimental evidence consistent with this analysis of elab-orative inference processing. Consider sentence 35:

35. After standing through the three-hour debate, the tired speakerwalked over to his chair.

One-quarter second after reading sentences such as 35, Keefe andMcDaniel’s (1993) participants viewed a word that represented anelaborative implication of the sentence, such as sat. Naming time forthe test word was about the same when it was implied and explicitlystated in the sentence, but longer when it followed a control sentencewith similar wording but quite different meaning. This outcome sug-gests that the readers had drawn an elaborative inference about sit-ting. However, when just one additional sentence intervened betweensentence 35 and the test word, naming time in the inference conditionresembled the control condition rather than the explicit condition.This result pattern generally coincides with the construction-inte-gration notion that ideas which initially appear in the coherence net-work may not endure integration. Immediate probing might suggestthat an elaborative inference has accompanied comprehension, but itmight not long survive in its competition with other encoded ideas.

MEMORY FOR TEXT

To benefit from understanding a text, one generally must remember it.The quality and quantity of people’s memory for discourse provides areflection of the mental processes of comprehension, representationsthat result, and changes in those representations over time. Text mem-ory has been examined in terms of question answering, sentence rec-ognition and verification, and free and cued recall of text. These tasks


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provide complementary and converging evidence about retrieval fromtext.

Stages of Question Answering

A prevailing research approach to question answering has been theidentification of the processing stages that contribute to successful an-swering. First, questions must be encoded to their propositional form.For example, What did Alice paint? would be denoted as (PAINT,AGENT:ALICE, PATIENT:?). The related yes–no question Did Alice

paint the roses? would be encoded as (PAINT, AGENT:ALICE, PA-TIENT:ROSES?). These two questions, respectively, request (a) theidentity of the painted object, and (b) the accuracy of ROSES. This no-tation highlights the particular importance, in question answering, ofdistinguishing between the given and new information (Clark &Haviland, 1977). For What did Alice paint?, it is given that Alicepainted something. The question requests the identity of the new infor-mation—namely, the painted objects.

Next, the answerer must categorize the question. Questions may in-terrogate the semantic roles of a question statement, such as its agent,instrument, or location (e.g., Fillmore, 1968). Other questions ad-dress complex relations between the question statement and relatedideas, including relations of cause (why?), reason (how?), and time(when?) (Graesser & Murachver, 1985; Lehnert, 1978; Trabasso etal., 1984). Both wh- (Who painted the roses?) and yes–no (Were the

roses painted by Alice?) questions may be formulated about any ofthese categories (Singer, 1990). The correlation between interrogativeterm and question category is imperfect: For example, why can queryeither a reason (Why did Alice paint the roses?) or a cause (Why were

the roses red?). Therefore, question categorization typically requiressyntactic and semantic analysis by the answerer.

Strategies of Question Answering. Strategy selection has been proposedto form a distinct stage of question answering. To answer a questionabout a text, one can attempt to retrieve the question statement frommemory or judge its plausibility (Camp, Lachman, & Lachman, 1980;Lehnert, 1977; Reder, 1982). For example, to answer Did Alice paint

the roses with a brush?, one could retrieve that statement from one’smemory of the story or evaluate it with reference to one’s knowledge ofpainting.

Strategy selection is guided by factors (a) extrinsic to the questionsuch as task instruction, and (b) intrinsic to the question such as itscurrent activation. The current activation of a question might be due to


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the recency of encountering the question statement (Reder, 1987). Inone study, participants read stories and subsequently answered ques-tions about them immediately or 2 days later under instructions eitherto retrieve or judge the plausibility of the questions (Reder, 1982). Theinfluence of instruction on strategy adoption is reflected in Table 3.1.Mean correct answer time was lower for the retrievers than for theplausibility judges in immediate testing, when the verbatim details ofthe story were still available, but the reverse pattern was found after 2days. However, delay not only influenced answer times, but also strat-egy: Answer time under the plausibility instruction was longer in im-mediate than delayed testing. This suggests that, contrary to their in-struction, the plausibility judges were attempting to retrieve thequestion statements in immediate testing. In general, people are versa-tile in their adjustment of answering strategy. They can adjust theirstrategy for each question on the basis of (a) advice (Reder, 1987), and(b) whether the question represents a recent or much earlier story(Reder, 1988; Singer & Gagnon, 1997).

Memory Search in Question Answering. Memory search in question an-swering has been clarified by J. Anderson’s (1974, 1976) fanning para-digm. In this paradigm, the participants learn facts like A teacher is in

the garage, An architect in the park, and A teacher is in the church. Intesting, recognition time varies directly with the number of facts inwhich the concepts of the test fact participated. For example, answertime is longer for A teacher is in the garage than for An architect is in

the park because teacher occurred in two facts and architect in onlyone. This effect is attributed to weaker concept–fact links for conceptsthat have participated in more facts (J. Anderson & Reder, 1999; butsee Radvansky, 1999, for a different view).

An important qualification of the fan effect is that people can focusmemory search on a queried category. In one study, people learnedthat characters like concepts in two categories: For example, the den-tist likes five (specified) cities and one animal. When asked, Does the


TABLE 3.1Mean Correct Answer Times (in Sec) as a Functionof Assigned Strategy and Test Delay (Reder, 1982)

Delay

Strategy 0 min 20 min 20 days

Retrieval 2.51 2.65 2.85

Plausibility judgment 3.21 2.68 2.61

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dentist like giraffes?, answer time is mainly determined by the num-ber of animals that the dentist likes (McCloskey & Bigler, 1980). Simi-larly, people can focus their memory search on an interrogated seman-tic role (Singer, Parbery, & Jakobson, 1988).

Answering questions about text clearly requires focused memorysearch: Why did Alice paint the roses? and How did Alice paint the

roses? demand different answers. Graesser (Graesser & Clark, 1985;Graesser & Murachver, 1985) proposed that each combination of text-statement and question categories is associated with distinct memorysearch procedures. For example, why-action questions (Why did Alice

paint the roses?) require the tracing of reason links between facts inthe backward direction, resulting in answers such as, “She wanted tohelp the gardeners.” Evaluation of this proposal revealed that a largeproportion of people’s answers to questions about narratives complywith the analysis, and that people assign higher goodness-of-answer

ratings to answers that conform with the analysis than those that donot (Graesser & Murachver, 1985).

In some instances, the focused search of memory may reveal thatthe text representation includes no information in the queried cate-gory. For example, the memory search initiated by Did Alice paint the

roses with a roller? may reveal no information about the instrumentthat Alice used. Frequently, this circumstance permits a rapid indica-tion that the answerer does not know the answer (Collins, Brown, &Larkin, 1980; Costermans, Lories, & Ansay, 1992; Glucksberg &McCloskey, 1981; Nelson & Narens, 1980; Singer, 1984). More gener-ally, there is evidence that people can assess the availability of perti-nent information before they retrieve it from memory (Reder, 1987).

Parallel Processes in Question Answering

The inspection of question-answering stages such as encoding, catego-rization, strategy selection, and memory search carries the sugges-tion that the stages are executed serially. Intuitively, however, itseems likely that the appearance of who at the outset of Who painted

the roses? would permit the question to be categorized, and the searchfor an agent to begin before the question was completely encoded. Ina test of a hypothesis of this sort, Robertson, Weber, and Ullman(1993) determined that reading time for sentences beginning with in-terrogative terms is longer than for control declarative sentences.They concluded that the memory search initiated by the interrogativeword occurred in parallel with question encoding and slowed the lat-ter process. These observations are generally consistent with propos-als of parallel processing in highly interconnected representations


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(Rumelhart & McClelland, 1986). Such processing is highly interactivein the sense that low- and high-level processes mutually influence oneanother. In the example of Robertson et al. (1993), it is presumed thatthere is interaction among encoding, categorization, and memory re-trieval. Likewise, Reder’s (1988) finding that the recency of the ques-tion statement affects strategy selection strongly suggests that memoryretrieval (which provides the index of recency) and strategy selectioninfluence one another. Contemporary question-answering theories ex-hibit many of the properties of connectionist computational models(Rumelhart & McClelland, 1986). According to the ACT-R model offact retrieval (J. Anderson, 1993; J. Anderson & Reder, 1999), for ex-ample, the connection strengths between a fact and its constituent con-cepts are determined by a connectionist learning rule. During subse-quent testing, activation is spread in parallel from all of the concepts ofthe test fact (e.g., A teacher is in the garage) until a fact is retrieved.

Recall and Recognition of Text

People frequently strive to remember text in circumstances in whichthey are not directly queried about it. The most open-ended text-retrieval task is termed free recall. Some robust phenomena were re-vealed in studies of students’ recall of newspaper articles. Kintsch andvan Dijk (1978) classified their participants’ recall responses as repro-

ductions, which corresponded to propositions expressed directly inthe original article, and reconstructions, which represented sensibleguesses but did not have counterpart propositions in the text. Figure3.1 reveals that text recall becomes systematically more reconstructivewith increased test delay; after 6 weeks, the two classes account for ap-proximately the same proportion of all recall responses. Singer (1982)measured a similar pattern—one that was hardly distinguishable be-tween reading in the laboratory and in the natural setting (i.e., beforeexperimental recruitment). Singer also reported that the participantsreproduced 26% of 124 text ideas in immediate recall and only 6% af-ter 6 weeks.

The convergence of reproduction and reconstruction in text recall isfurther clarified by people’s text recognition. Text recognition is influ-enced by the relationship between the text and the test sentence—thetest sentence can express an explicit text idea, paraphrase the text, rep-resent a reasonable inference based on the text, or constitute an inac-curate distractor. In immediate recognition, the acceptance rate forexplicit items is considerably higher than for paraphrases and infer-ences. With increased delay, the explicit acceptance rate does not de-cline much, but the rates for paraphrases and inferences rises toward


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the explicit rate, and even the acceptance of distractors increasessomewhat (Kintsch et al., 1990; Reder, 1982). This profile is illus-trated in Fig. 3.2.

This striking pattern can be explained as follows. In immediate test-ing, participants have reasonably detailed representations of the sur-face form, textbase, and situation model at their disposal. The recogni-tion rate reflects the number of levels with which the test item isconsistent. Thus, explicit items, which are consistent with all three lev-els, are accepted the most often, followed by paraphrased items, whichdo not match the surface form, but are consistent with the textbase andsituation model. Finally, inferences are accepted the least often be-cause they match neither the surface nor textbase representations andare consistent with the situation model only (Kintsch, Welsch,Schmalhofer, & Zimny, 1990; Schmalhofer & Glavanov, 1986). Withincreased delay, the verbatim form of the message is all but forgotten.Likewise, there is considerable loss of the idea content (textbase) of thetext over time (Kintsch & van Dijk, 1978; Singer, 1982). Therefore, de-layed recognition increasingly depends on the robust situation model.


FIG. 3.1. Reproductive and reconstructive text recall as a function of text delay. From

“Toward a Model of Text Comprehension and Production” by W. Kintsch & T. A. van Dijk,

1978, Psychological Review, 85, p. 385. Copyright 1978 by the American Psychological

Association. Adapted with permission.

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As a result, the relative recognition advantages of explicit and para-phrased test items are lost.

The complex profile in text recognition meshes with the proposal,scrutinized earlier, that text comprehension results in multiple levelsof representation. It also meshes with the convergence, with increaseddelay, of reproduction and reconstruction in text recall (see Fig. 3.1).In recall, like recognition, the participant must increasingly rely on thesituational representation as the retention interval grows. Insofar asthe situation model represents the blending of text information andgeneral knowledge, many delayed recall responses are reconstruc-tions—they do not correspond to a text proposition.

CLOSING COMMENTS

We have reviewed the psychological theories and methods of psycho-logical text comprehension research. Text comprehension proceeds incycles and involves the simultaneous construction of three levels ofrepresentation: surface structure, textbase, and situation model. Keyamong these levels is the situation model. The situation model is amental representation of the state of affairs denoted by a text. In mostcases, the purpose of text comprehension is the construction of a situa-


FIG. 3.2. Proportion acceptance as a function of delay. From “Sentence Memory: A The-

oretical Analysis” by W. Kintsch, D. Welsch, F. Schmalhofer, & S. Zimny, 1990, Journal ofMemory and Language, 29, p. 139. Copyright 1990 by Academic Press, Inc. Adapted

with permission.

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tion model. Several decades of research have revealed text comprehen-sion to be a highly complex cognitive process that involves most areasof cognition: perception, working memory, long-term memory, prob-lem solving, and imagery.

Research is currently underway to study the neural mechanismsthat subserve text comprehension. Furthermore, the traditional as-sumption that language comprehension involves the disembodiedconstruction of abstract propositional networks is being challenged byrecent proposals and supporting evidence that language comprehen-sion involves analog, perceptual representations that reflect how we,as humans, interact with our environments (Barsalou, 1999;Glenberg, 1997; Stanfield & Zwaan, 2001; Zwaan, Stanfield, & Yaxley,2002). It is to be expected that these developments will make funda-mental contributions to the understanding of what goes on in ourmind/brains when we process the black marks on white.

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